An analysis on service eciency in nursing homes and spatio-temporal variation in China

Background: Pension services market in China is still at the early stage, problems like low service eciency and low quality of nursing care already exist. So it is inevitable to analyze the eciency and productivity and spatio-temporal variation, as well as its inuencing factors in nursing homes all over the country. Methods: Data Envelopment Analysis (DEA) and Tobit model were applied to integrate several quality measures into a comprehensive benchmarking model. We present nationwide results and analyze the spatio-temporal distribution of the (technical eciency) TE and productivity of nursing homes among Eastern, Central and Western of China. Furthermore, Tobit model was performed to explore factors associated with TE. Results The average TE, pure technical eciency (PTE) and scale eciency (SE) of nursing homes for the 5-year period were 0.909. 0.928 and 0.979, respectively. The TE and SE decreased from 2012 to 2014, but improved after 2014. TE is 0.98 in the Eastern region, 0.93 of that in the Central region and 0.91 of that in the Western region, with a decrease range of 2%, 7% and 9%, respectively. The average improvement range of the ve input indexes of the non-DEA effective nursing homes was 27.26%, 20.62%, 19.77%, 22.04%, and 38.84%, respectively. The inuencing factors of eciency value of nursing homes indicated that if there are more social workers, more patients in the nursing homes, and more employees who are aged 56 and above, the TE and productivity of nursing homes will be higher. TE low proportion effective TE. TE SE 2012 2014, which

In order to solve the problems brought about by the aging of the population, Chinese government has promulgated relevant laws, regulations and policies to encourage social forces to set up nursing homes for the older people. It has arranged a total investment of 9.17 billion yuan in the central budget, focused on supporting the construction of nursing homes for the elderly and facilities for elderly care with integrated medical care. By the end of January 2019, there were 163,800 nursing homes and various types of facilities in the country, of which 29,700 were nursing homes, 46,600 were community service facilities for elderly, and 87,300 were mutual care facilities. A total of 7.46 million beds were provided for the various types of elderly care services, and among them, there are 3.928 million beds in nursing homes7. Total beds account for 3% of the country's older population. Generally, developed countries can reach 5% -7%.
From the calculation, the number of beds required should be at least 12.45 million to accommodate China's elderly, who are in the need of LTC8. It means that the overall shortage of beds for the elderly, there is also a large space to adjust, showing a very obvious contradiction between supply and demand. Moreover, substantive matters in residential care as they evolved in today's long-term health care environment due to the late implementation of residential care in China, which is still in the early stages of development, LTCFs are currently facing problems and di culties like large investment, slow returns, low pro ts, and high risks. Also, service levels and quality need to be improved urgently. In the "Research Report on the Development of China's Pension Institutions," it was pointed out that about 257 residential cares in 12 interviewed cities, including Tianjin, Harbin, Jinan, and Wuhan, etc., among those, about 48.1% of the operating conditions were basically the same, 32.5% were in de cit, and only 19.4% had a surplus9.A survey showed that there were more than 30 private residential cares in the city of Chengdu, with a total of more than 8,000 beds with only 4,500 older people10. Even though the number of people who chose to take care of the elderly will vary due to the changing seasons, the overall occupancy is still not that high. The occupancy rate of most private institutions is far lower than that of the state-owned pension institutions11.
Various contradictions between supply and demand still inevitably occur in China, such as the serious shortage of beds, the corresponding number of nursing staff, the low level of professionalism, small-infrastructure scale of residential care, and the lack of risk management system and the service evaluation system for elderly care12. In addition, the survey showed that compared with Eastern and other regions, the beds utilization rate is signi cantly higher than that in the Western region; the available beds in the central urban area are di cult for the older people to get, while the bed utilization rate in the private pension institutions in the suburbs is very low13. Therefore, how to effectively improve the service quality, service e ciency, and also the output e ciency of nursing homes in various provinces in the process of increasing investment and rationally allocating service resources have become a focus of attention by the government and academia.
With the deepening of the research on the service quality of nursing homes, foreign scholars began to pay more attention to the problem of inputs and outputs of the nursing homes at the same time. A sample survey was conducted in nursing homes in certain areas of Finland, Italy, Japan and Norway respectively. Also, the relative e ciency and in uencing factors were studied, based on explorations on application of Data Envelopment Analysis (DEA) method and regression model14-17. E ciency can be simply used as a tool to explain the relationship between the inputs and outputs. And even TE can be applied to nd out whether any waste can be eliminated without worsening any input or output18. DEA was de ned as an advantageous non-parametric technique for evaluating performance in terms of relative e ciency in the presence of multiple inputs and outputs19.
Although most of researches in developed countries on the service e ciency of pension service institutions are relatively complete, their research approaches and results may not be applicable to China due to different levels of development. There exist amounts of shortcomings in the researches related to the e ciency of China's nursing homes. For example, many studies in China still focused on the theoretical researches for the institutional planning of LTCFs, including the exploration of institution building and service standards, service contents, and service personnel.
Moreover, many researches mainly focused on individual nursing home in a certain province or city. Also, some of the research methods are investigation and research methods, with less statistical data are used to quantitatively study the overall e ciency of pension service institutions in different regions of China, which cannot provide scienti c basis for promoting the overall healthy development of the nursing homes in China from a macro perspective. In addition, most of the previous researches use the cross-sectional data to apply the single stage and DEA Tobit two-stage models in conducting a regional static research. Therefore, a panel data was used to analyze the service e ciency of nursing homes of China's 31 provinces and municipalities and its spatio-temporal variation, based on the input-oriented DEA. DEA method was applied to make comprehensive evaluation of service e ciency for regional nursing homes and its spatio-temporal variation in China, combined with Tobit model to analyze the in uencing factors of service e ciency. The purpose of this study is to provide an evidence for decision-making by integrating and optimizing resource allocation, as well as to promote countermeasures for the transition of LTCFs, and then getting sustainable development of increasing service quality to comprehensively improve the service e ciency of LTCFs.

Data
The original data set comprises all of the nursing homes in 31 provinces of China. These LTCFs were observed from 2012 to 2016. Data were collected from the China Civil Affairs statistical yearbook20, which is related to the social services in each year among all of China's provinces.

Data Envelopment Analysis
DEA is an advantageous non-parametric technique for measuring the relative e ciency of homogeneous decision making units (DMUs) because of its advantages in dealing with multi-input and multi-output indicators, such as the e ciency evaluation of medical services19. DEA can handle multiple input and chromatography at the same time, for example, a number of output e ciency does not need to presuppose function and parameters. On the other hand, the input and output variables of weight coe cient is produced by the mathematical programming according to the data. Also, the DEA doesn't need to set arti cial weight coe cient, without being in uenced by subjective factors e ciency value21. Furthermore, it has nothing to do with the dimension in selecting the suitable input and output items. Also, it avoids calculating the standard cost of each service because it can convert multiple inputs and outputs into numerators and denominators of the e ciency ratio without converting them into the same monetary units22. Therefore, the DEA model can be regarded as the better choice for the purpose and current situation of the research on the comprehensive e ciency of nursing homes. Selection of the model 1. Charnes-Cooper-Rhodes (CCR)and Banker-Charnes Cooper (BCC)model DEA is a mathematical programming approach for the empirical evaluation of the relative e ciency of a DMU based on observed inputs and outputs for a group of similar DMUs. It calculates a maximal performance measure for each DMU relative to all other DMUs in the observed population with the sole requirement that each DMU lie on or below the frontier23. The traditional DEA model mainly included CCR model based on constant returns to scale and BCC model with variable returns to scale 21 . Both models were radial models that rely on the basic assumption that inputs must be reduced as much as possible, while outputs must be maximized also as much as possible to serve as an advantage in the absence of the actual production process. However, the redial CCR model24and BCC model25 suffers from one shortcoming; they neglect the slacks in the evaluation of e ciencies. To overcome this shortcoming e ciency scores can be computed using the ''slack based'' non-radial and non-oriented DEA model26. This paper measures the overall technical e ciency (TE), pure technical e ciency (PTE) and scale e ciency (SE) of 31 provinces of China. The TE refers to the extent to which a DMU can produce the maximum output from its chosen combination of inputs and scale e ciency refers to sub optimal activity level.

2.SBM model
It is known as the Additive Model (AM) or a slack-based model (SBM) and this is based on input and output slacks27. This model allows managers to work on both inputs and outputs to achieve e ciency. In this study, a non-oriented and non-radial model known as SBM-DEA model has been used27. BCC model and SBM model all choose input-oriented. The reason was that the output of LTCFs was the care of the elderly, not controlled by the LTCFs themselves. LTCFs can only improve service e ciency by adjusting input. Therefore, we used the input-oriented model in this demonstration. An input-oriented DEA model was used to compute TE scores of nursing care can be expressed by the following formula.
In the case of θ = 1, S − = 0, S + = 0, the nursing home is fully e cient, whereas θ < 1 means that a nursing home is ine cient. BCC model adds constraint conditions on the basis of CCR model: At this time, it means that the return on scale of DMU remains unchanged and reaches the maximum output scale. In addition, when , this means that returns to scale are increasing. If the input of DMU is appropriately increased on the basis of the original input, the output will be increased by a higher proportion. Whereas , it means diminishing returns to scale, and increasing input does not lead to a higher proportion of output. In addition, for the formula of SBM-DEA, please refer to the paper written by Mogha SK for details28. Due to TE values have truncated characteristics and e ciency values are relative, using general multiple regression models results in bias and parameter estimation instability29.

Malmquist model
Productivity measures changes in a production unit's e ciency in transforming inputs into outputs from time t to time t+130. It was generally used in panel data. MPI was named after Malmquist31 and was introduced by Caves, Christensen and Diewert to evaluate productivity changes among different production units32. MPI (also called total factor productivity changes (TFPC)) can be decomposed into technical e ciency changes (TEC) and technological changes (TC). TEC can also be decomposed into pure technical e ciency changes (PTEC) and scale e ciency changes (SEC)33. TFPC = TEC × TC= (PTEC × SEC) × TC

DEA classi cation
This method aims to nd changes in the inputs of a DMU so that it can be classi ed into a different and desirable class. In this paper, we adhere to all the assumptions made by the Troutt et al.34 and the Seiford and Zhu35 studies to classify the DMUs into Eastern, Central and Western regions. Assuming non-negative inputs and an output of unity for all the acceptable cases and using the DEA model proposed by Banker et al.36.

Input-output indicators
There is no consensus conclusion on the choice of input-output variables for the operation e ciency of old-age institutions37 , 38. Based on the theory of production factors in economics, the principles of representativeness, independence, and operability of the evaluation index selection, input factors can be divided into capital, labor, and material inputs, and output factors can be divided into economic bene ts and social bene ts39. As LTCFs were laborintensive industries. Scholars usually used xed assets as capital input index and various types of institutional staff as human input index40 , 41. The general rule of thumb is that the number of DMUs should be at least twice the sum of the input-output indicators. As In terms of material inputs, the actual number of beds was a resource that was easily controlled by managers in material resources. A number of studies considered the number of beds, the number of institutions and the original price of xed assets as indicators of material resources42 , 18 , 43. In addition, the older care industry is a labor-intensive industry, the capital of the labor substitution is very small. Comprehensively, choosing human capital as input index is an appropriate index for e ciency comparison, like the main care resources including the number of social workers and the number of employees44. Therefore, the input indicators established in this article are the number of institutions, the number of employees at the end of the year, the original price of xed assets, the number of social workers, the number of beds at the end of the year (please refer to table 1).
Output indicators are considered to be the most important factors in evaluating the quality and quantity of long-term care services45. In the selection of service e ciency evaluation indexes of nursing homes, most researches only consider the input of human capital or nancial capital, like operating income, but fail to consider the service quality of pension service institutions comprehensively as the outcome index of input-output e ciency in the process of building institutions46 , 47. In addition, the quality of service directly affects the nal effect of elderly care and the development prospects of the residential care. The health condition of the older people is very important for the improvement and development of LTCFs, and have a direct impact on the nal performance evaluation results of the service, like the number of rehabilitation and medical outpatients. Furthermore, the service quality can be re ected by the number of disabled, the number of partially disabled the number of complete independents and in residential cares at the end of the year. Those older people were measured by Barthel index. Therefore, the output indexes should include operating income, the number of disabled, the number of partially disabled and the number of independents in residential cares at the end of the year, and also the number of rehabilitation and medical outpatients (refer to table 1). Table 2 presented summary statistics of input-output variables.

Results
Comparison of the e ciency of residential care between provinces and municipalities in China from 2012 to 2016.  Table 4 showed that the traditional CCR model, BCC model, and SE model were used to count the number of nursing homes with effective TE, PTE and SE in various provinces and municipalities. As shown in gure 3, the number of nursing homes with effective TE, PTE and SE in all provinces decreased from 2012 to 2014. Then, the number gradually rose since 2014. The number of nursing homes with DEA-effective provinces and municipalities were at the lowest in 2014, while a trend showed an initial decrease and then an increase in the process.
SBM model was used to calculate the changes in the DEA e ciency value of the LTCF in various provinces and municipalities across the country  Table 6 showed that, from 2012 to 2016, the average TEC was 1.001, and the index increased or decreased by 1.1% to 26.4% in each year, with a large change range, among which the increase in 2015 was up to 26.4%. The average value of TC is 0.983, with an increase or decrease rate of less than 4% per year. After 2015, the trend of steady rise remains stable. The average PTEC was 1.003, with a sharp increase of 19.8% in 2015.The average value of SEC is 0.988, which is in a stable development state. The average value of TFPC was 0.984, and the maximum increase of TFPC in 2015 was 31.8%. As shown in gure 3, the ve e ciency values of LTCFs in combined total of 31 provinces and municipalities reached the lowest in 2014 and the highest in 2015. During this period, the results showed a trend that initially went upward and then eventually downward.
DEA Classi cation was used to cpmpare the DEA e ciency value of the LTCFs in Eastern, Central and Western regions The regions were divided into three major classi cations, namely Eastern (developed), Central (generally developed), and Western (underdeveloped) regions, which refers to the regional division in the China Health Statistics Yearbook. Table 7 presents the means of TE of nursing homes in East, Central and West were 0.98, 0.93, 0.91 respectively, with a decrease range of 2%, 7% and 9%, respectively; the means of PTE of nursing homes in Eastern, Central and Western regions were 0.98, 0.95, 0.94 respectively, with a decrease range of 2%, 5% and6%, respectively; the means of SE of nursing homes in Eastern, Central and Western were 0.99, 0.99, 0.96 respectively, with a decrease range of 1%, 1% and 4%, respectively  Based on input-oriented (assuming that the output is unchanged), the CCR model was used to calculate the projected value of non-effective LTCFs in all provinces and municipalities in terms of inputs, the number of adjustments (actual value-projected value) , and adjustment ratio[(adjusted number / actual value) ×100% ]of 15 non-effective LTCFs in provinces and municipalities in terms of investment are calculated. (Table 9) The results indicated that in 2016, the non-e cient LTCFs in all provinces and municipalities, the number of institutions, the number of employees by end of the year, the total value of assets, the number of social workers, and the number of beds at the end of the year have been redundant in varying degrees. If the original output of 31 provinces and municipalities of China is maintained, and the input structure and DEA is effectively optimized, it will result in the following: the number of beds in China's LTCFs can be reduced by 360038.9, the number of institutions can be reduced by 3464.9, and the number of social work can be reduced by 497, the total value of assets can be reduced by 87,0381.7 yuan, and the number of employees can be reduced by 3,5465.9.
The e ciency values calculated by SBM and BCC models were used as the Tobit in uencing factors in regression analysis The LTCFs with a greater number of women, social workers, and annual older people have a greater e ciency value. Also, age gets to have an effect on the e ciency value. The results showed that more people within the age range of 35 and below, and 46 to 55 in the LTCFs of nursing homes, a lower e ciency value is. However, if the LTCFs with the greater number of people over the age of 56, the e ciency value will be higher. With regard to the facilities construction, since pension service facilities were included in 20 public project each yea, 385 construction pension agency, 1191 community elderly day care centers, and 392 community canteens and catering center were established during the period. As for the standardized management, it has compiled local standards such as service standards for pension institutions,classi cation and evaluation of pension institutions, and formulated implementation rules for the establishment of pension institutions and measures for administration pension institutions to standardize the management system of pension institutions. Moreover, service reform was carried out by the national pension service comprehensive reform since 201451. A series of national policies laid a great foundation to promote economy and development motivation of pension institution by improving the service speci cation standards and national investment to expand the scale of the institution.

Spatial, temporal and regional distribution analysis of the operational effectiveness of nursing homes in China
Nursing homes in Beijing, Guangdong, Tianjin and other economically developed provinces and municipalities have always been technically e cient from 2012 to 2016. Based on enacted policies of Tianjin, it has removed and closed nearly 30 seriously substandard pension institutions by encouraging renovation of re ghting for nursing homes. Referring to the guidelines for the quality inspection of nursing homes nationwide, the policy of "one district one case" and "one hospital one policy" have been adopted to make up for some existing shortcomings53,54. The results showed that nursing homes in Shandong province changed from being technical ine ciency in 2014 to an effective TE in 2015. The existing reasons behind might be that in 2014, the "China's 12th ve-year plan for the development of undertakings for the elderly", and "Suggestions on accelerating the development of the elderly care service industry" were comprehensively implemented. Furthermore, the government has allocated services development special funds of 2.4 billion yuan to support the development of pension services industry of market-oriented polit area in Jilin, Shandong and other eight provinces55. All of these were done to effectively promote the nursing homes in Shandong in one year by means of a rapid rise from being technical ine cient into technical e cient.
TFPC of the sample nursing homes from 31 provinces declined by 2.14% during the 5-year period. Although TEC has improved with an average of 0.1%, the average1.7% decrease of TC leads to the decrease of TFPC. Therefore, improving TC to boost TFPC is essential. The results obtained in the nursing homes of 31 provinces are not distinct. PTE of nursing homes is 0.98 in the Eastern region, 0.95 of that in the Central region, and 0.94 of that in the Western region. SE of nursing homes is 0.99 in the Eastern region, 0.99 of that in the Central region, and 0.96 of that in the Western region. However, TE of nursing homes is 0.98 in the Eastern region, 0.93 of that in the Central region, and 0.91 of that in the Western region, with a decrease range of 2%, 7%, and 9%, respectively. This result presented that among these three e ciency values, TE of nursing homes is lowest due to the higher SE of nursing homes in China. The possible reason behind might be due to the expansion of institution scale, which fails to compensate the adverse effects of having insu cient technology during operations of the institution, and then accompanied with ine cient utilization and wasting of input resources. It indicated that there is a need to downsize the scale of nursing homes appropriately to t the levels of technology among the different regions of China. In addition, the e ciency value of the three regions showed an upward trend after 2014. In the Central and Western regions, the service e ciency of institutions has decreased greatly, which is similar to the research results of Qian Haiyan. It was concluded that there existed signi cant differences in the e ciency of nursing homes among Eastern, Central and Western regions and also between urban and rural areas according to DEA model analysis58. There are also regional differences in the service e ciency of medical institutions, which are related to the results of the imbalance allocation of medical resources and multiple levels of economic development among Eastern, Central, and Western regions of China59. The level of economic development in the Central region is only followed to that of the Eastern region, however, the service e ciency of the nursing homes in the Central region remains much low. The service e ciency of the old-age care service institutions in the central area urgently needs to be improved overall. It is to optimize organization management and break the development bottleneck caused by low TE. Regarding the Western region, health resources are fewer than other regions with lower service e ciency than that in Central region. Attentions should be paid to the balanced development of the Eastern, Central and western regions. In the future, this should be the main concern.
4. The adjustment of projected value for non-DEA effective nursing homes in 31 provinces and municipalities of China in 2016 The average improvement range of the ve input indexes of the non-DEA effective nursing homes of 31 provinces was 27.26%, 20.62%, 19.77%, 22.04%, and 38.84%, respectively. Five input indexes included the number of institutions, the number of employees at the end of the year, the original price of xed assets, the number of social workers, the number of beds at the end of the year. It was noticeable that the number of beds at the end of the year in in non-DEA effective nursing homes needs to be improved the most, which indicated that the construction scale of nursing homes in some provinces were blindly expanded, while the beds occupancy rate of private nursing homes was lower, with amount of idle beds. All of these caused wasting amount of expenditure and capital. In addition, compared with Eastern and other regions, the beds utilization rate was signi cantly higher than that in the Western region. The available beds in the central urban area are di cult for the older people to get, while the bed utilization rate in the private nursing homes in the suburbs was very low13. A survey showed that there were more than 30 private residential cares in Chengdu, with a total of more than 8,000 beds with only 4,500 older people10. It was presented that only about half of the beds in the elderly care institutions in Chengdu urban area were applied, and 32.5% of the urban institutions in Tianjin, Harbin, Jinan, Wuhan and other cities are in de cit for a whole year, with an overall low bed occupancy rate9. The number of social workers also needs to be improved by 22%, which is consistent with the current situation that China's integrated medical and nursing system is still far from perfect, the corresponding number of nursing staff is still small, and also the professional level is very low. As the service quality of the older adults care depends on the mainstay resources, the competence of social workers is an important re ection of the overall quality of nursing homes. The development of social workers needs the support of the government because of its relevant policies it can introduce. Also, it needs to attach importance to the prospects being trained in social work-related majors in colleges, universities and society, so as to ensure that highly quali ed social workers can play an important role in manpower for the elderly care service60.

The analysis of factors affecting the e ciency of nursing homes, based on Tobit model
The e ciency value of nursing homes in 31 provinces was in uenced respectively by gender and age of employees, the number of social workers, the number of older people in a year. If there are more women in nursing homes, more social workers, and more patients at the end of the year, the TE and productivity of nursing homes will be higher. The age of the employees also has an in uence on the e ciency value, if there are more employees in the age of 35 and below, and the more within the age of 46 to 55, the e ciency value in nursing homes will be lower. On the other hand, the more employees who are aged 56 and above, the e ciency value gets to be higher. Mueller et al. (2006) and Castle (2008) consider that the level of professional ability of nursing staff can make a great effect on the service quality and the life quality of the elderly in nursing homes61 , . The research also showed that relatively the young nursing staff, who are mainly women around 40 years old, do not have any sense of longevity with their jobs due to their lack of su cient skills. However, after long years of working experiences, their level of skills could eventually improve. Also, the nursing staff's level of professionalism can be improved with more practical experiences, especially for female nurses. Furthermore, they are expected to have more patience and dedication to elderly care, so that the elderly can obtain better service quality for them to be able to practice self-care ability; thus, improving the outputs and e ciency of nursing homes in the process62. A study showed that social workers play an important role in resource integration, emotional supplement, communication and coordination, spiritual support, social function and also in managing of nursing homes. This situation re ects the inadequacy of competent personnel, which resulted in the decline of the e ciency in the social work services63. The development of social workers needs to be strengthened the supported by the government by setting up policies, standards and norms to ensure that highly competent social workers can ll in the manpower needed by elderly service institutions. Rosko et al. (1995) conducted a research to evaluate the service e ciency and in uencing factors in nursing homes in Pennsylvania, which presented that the occupancy rate of institutions affected the service e ciency64. The bed occupancy rate signi cantly affects the service e ciency, that is, for every 1% increase in occupancy rate, the comprehensive e ciency of pension institutions will increase by 0.871%65. As the pension industry development information asymmetry, it is easy to nd the phenomenon of vacant beds and a bed at in different regions. However, the number of patients at the end of the year to some extent, can re ect the bed occupancy levels in nursing homes, the recognition of acceptance and the levels of service quality of the elderly.

Strengths And Limitations
This study introduced the DEA method to comprehensively evaluate the performance of nursing homes in China, based on panel data, by analyzing the e ciency and productivity of nursing homes and spatio-temporal variation, as well as its in uencing factors in nursing homes all over the country. Furthermore, this data is representative of the status quo in China. Therefore, this study is representative of the overall country and can improve the understanding of the complex e ciency issues in the different regions of China.
However, we believe that this study can still be improved and extended in a number of directions. The rst limitation is the limited variables related to the outputs and inputs because of data limitations. Even though we compensate the limitations of previous studies to include outcome quality, further research should be conducted and include more variables of the quality of care to comprehensively analyze the e ciency of the nursing homes. As another limitation of this study, since DEA is a data-oriented method, the e ciency values obtained are directly affected by the combination of inputs and outputs applied. In this study, we calculated e ciency scores using ve nance and manpower variables as inputs and ve quality-of-care and nance variables as outputs, and we de ned the scores as TE of nursing care, according to the study setting, data availability, and researcher decision making. However, further research should explore the standardized criteria and mechanism to appropriately selected to measure the e ciency of decision-making units, for example, qualitative studies could be applied to explore the reasons for the changes in TE and productivity.

Conclusions
This study provides an empirical representation of the e ciency and productivity changes of nursing homes in the process of advancing the marketization of pension services in China. The TFPC of nursing homes in 21 provinces and municipalities experienced a decrease in productivity from 2012 to 2016, while the uctuation of SEC was not as strong as that of TC and PTEC, with the relatively smaller SEC. The improvement of the productivity and TE of nursing homes has not kept pace with the development of the scale of institutions. So the adverse alteration in TC and PTEC should be emphasized. Compared with Western and Central regions, the TE, PTE, SE of nursing homes in Eastern regions were all the highest, followed by Western region and Central region. Among these three e ciency values, TE of nursing homes is lowest due to the higher SE of nursing homes in China. The results showed that the in uencing factors of in nursing homes might include gender and age of employees, the number of social workers, the number of older people in a year. It was suggested that the country have to attach great importance to the cultivation and improvement of the professional level, educational background and experiences of the professionals for elderly care, so as to meet the diversi ed needs of different types of elderly groups.